Data Mining for Disease Diagnosis: A Review of Machine Learning Approaches in Healthcare

Data mining and machine learning tools are essential healthcare instruments that transform medical diagnosis and disease forecasting procedures. Medical datasets continue to grow in availability, which leads medical experts to adopt various computational techniques to find meaningful patterns for clinical decision support. This research conducts a thorough analysis of Machine Learning (ML) methodologies utilized in disease diagnosis while analyzing their beneficial characteristics together with their boundaries and practical implementation situations. This article examines both supervised and unsupervised learning approaches, deep learning capabilities, and hybrid modeling techniques for enhancing accuracy levels. Medical data mining procedures include extensive examinations of selection techniques along with approaches for classification and performance measurement methods. The use of data mining techniques is evaluated for cardiovascular disease diagnosis together with cancer detection and handling of diabetes and neurological conditions. Our analysis of different algorithms focuses on describing key problems, including unbalanced data sets, a lack of interpretation methods, and ethical uncertainties. The discussion ends with proposed directions for further ML-based disease diagnosis system development.